Methods and arrangements to detect a payment instrument malfunction

ABSTRACT

Logic may detect a malfunctioning payment interface on a customer&#39;s payment instrument. Logic may receive transaction data about at least one transaction performed via a payment instrument associated with a customer. The transaction data may indicate a payment interface of the payment instrument through which the customer conducted the at least one transaction and the payment instrument may comprise one or more payment interfaces. Logic may determine, by a model, based on the transaction data, a probability of a malfunction by at least one interface of the one or more payment interfaces. The model may be trained based on a pattern of transactions associated with malfunctioning payment instruments. Logic may compare the probability of the transaction against a threshold. And logic may determine whether to contact a customer associated with the payment instrument, based comparison of the probability of the transaction against the threshold.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/182,578, entitled “METHODS AND ARRANGEMENTS TO DETECT A PAYMENTINSTRUMENT MALFUNCTION” filed on Nov. 6, 2018. The contents of theaforementioned application are incorporated herein by reference.

TECHNICAL FIELD

Embodiments described herein are in the field of malfunction detection.More particularly, the embodiments relate to methods and arrangements todetect a malfunctioning payment interface of a payment instrument basedon transaction data.

BACKGROUND

A modern credit card has three primary methods of making an in-persontransaction with a merchant: tap, chip, and swipe. Each method relies ondifferent technology embedded within the credit card, and one paymentmethod may fail while the other two continue to function. For instance,an induction loop within the credit card may break, causing the tapfunctionality to stop working, but the chip and swipe functionality maybe unaffected.

Such malfunctions may not be detectable through visual inspection of thecredit card, so the customer may not realize why a transaction with thetap is failing. This may lead to unsatisfactory customer experience.

SUMMARY

Embodiments may include methods and arrangements such as methods,devices, apparatuses, systems, storage media, and the like. For example,a first embodiment may include an apparatus. The apparatus may comprisememory; and logic circuitry coupled with the memory to receivetransaction data about at least one transaction performed via a paymentinstrument associated with a customer. The transaction data may indicatea payment interface of the payment instrument through which the customerconducted the at least one transaction. The payment instrument maycomprise one or more payment interfaces. The logic circuitry coupledwith the memory may determine, by a model, based on the transactiondata, a probability of a malfunction by at least one interface of theone or more payment interfaces, wherein the model is trained based on apattern of transactions associated with malfunctioning paymentinstruments. The logic circuitry coupled with the memory may compare theprobability of the malfunction against a threshold and determine whetherthe at least one interface is malfunctioning based comparison of theprobability of the malfunction against the threshold.

A second embodiment may include a non-transitory storage mediumcontaining instructions, which when executed by a processor, cause theprocessor to perform operations. The operations may, based ontransaction data about at least one transaction performed via a paymentinstrument associated with a customer, the transaction data to indicatea payment interface of the payment instrument through which the customerconducted the at least one transaction, the payment instrumentcomprising one or more payment interfaces; determine, by a model, adeviation from a transaction pattern by at least one interface of theone or more payment interfaces. The model is trained based on thetransaction data. The transaction data may be indicative of a pastspending behavior of the customer. And the operations may determine,based on a threshold, to contact a customer associated with the paymentinstrument.

A third embodiment may include a system. The system may comprise memoryand logic circuitry coupled with the memory to provide transaction dataassociated with more than one customer. The logic circuitry coupled withthe memory may identify for multiple customers of the more than onecustomer, a request to obtain a new payment instrument. The logiccircuitry coupled with the memory may determine, for multiple customersof the more than one customer, a set of transaction data indicative of atransaction pattern leading to the request to obtain the new paymentinstrument. And the logic circuitry coupled with the memory may train amodel based on the set of transaction data for the multiple customers,the model to output, in inference mode, a probability based on adeviation from transaction patterns that lead to the requests to obtainnew payment instruments.

BRIEF DESCRIPTION I/F THE DRAWINGS

FIGS. 1A-C depict embodiments of systems including servers, networks,and point-of-sale terminals to detect malfunctioning payment interfacesof payment instruments;

FIG. 1B depicts an embodiment of a payment instrument with multiplepayment interfaces to process transactions with point-of-sale terminalssuch as the point-of-sale terminals in FIG. 1A;

FIG. 1C depicts an embodiment of an apparatus such as a server or othercomputer to detect malfunctioning payment interfaces of paymentinstruments;

FIG. 1D depicts an embodiment of a neural network of a malfunctiondetection logic circuitry, such as malfunction detection logic circuitryillustrated in FIGS. 1A, 1C, and 2;

FIG. 2 depicts an embodiment of malfunction detection logic circuitry,such as the malfunction detection logic circuitry shown in FIGS. 1A, 1C,and 2;

FIGS. 3A-E depict flowcharts of embodiments to pretrain a model, performmalfunction detection with a model, and communicate with a customer, bymalfunction detection logic circuitry, such as the malfunction detectionlogic circuitry shown in FIGS. 1A, 1C, and 2;

FIG. 4 depicts an embodiment of a system including a multiple-processorplatform, a chipset, buses, and accessories such as the server shown inFIGS. 1A, 1C, and 2; and

FIGS. 5-6 depict embodiments of a storage medium and a computingplatform such as the server and the point-of-sale devices shown in FIGS.1A-B.

DETAILED DESCRIPTION I/F EMBODIMENTS

The following is a detailed description of embodiments depicted in thedrawings. The detailed description covers all modifications,equivalents, and alternatives falling within the appended claims.

Customers may begin to rely on one or more payments instruments such ascredit cards to perform transactions. Many customers have typical,repetitive, or periodic expenses for which they rely on one or morecredit cards. For instance, customers may use one or more credit cardsto purchase gas for their vehicles once a week, eat lunch at arestaurant or cafeteria a few times a week, pick up groceries a fewtimes a month, and/or the like. Some customers fall into a routine inwhich they use the same payment instrument to perform most of theday-to-day transactions because they prefer use of that card for onereason or another.

Many payment instruments are plastic or metal credit cards that includepayment interfaces of various technologies to conduct transactions forcustomers. The payment interfaces may include a magnetic stripeincluding information associated with the customer that a card readercan read to process a transaction. Magnetic stripes containmagnetically-stored information for conducting a transaction and aretypically applied to credit cards as a hot foil tape. In manyembodiments, the credit cards include a high-coercivity magnetic stripethat requires a higher magnetic energy to record (e.g., 4000 oersted(Oe)) than medium-coercivity (e.g., 2750 Oe) and low-coercivity (e.g.,300 Oe). The higher magnetic energy to record may improve the resistanceof the magnetic stripe to erasure but, if a card becomes exposed to ahigh magnetic energy or becomes worn through repeated use or other, themagnetic stripe may become difficult to read. In fact, the magneticstripe may slowly deteriorate, causing card readers of point-of-sale(POS) terminals to fail.

Payment instruments may also comprise a chip such as a microchip withcontacts as a payment interface and such payment instruments are oftenreferred to as Chip and PIN (personal identification number) or Chip andSignature cards. The chip may be a processor that is a contacted paymentinterface. With the Chip and PIN cards, the POS terminal, if it has thecorresponding capabilities, may verify the identity of the customer witha PIN via the chip, whereas Chip and Signature cards require a signatureto verify the identity of the customer. In some embodiments, the chipmay also generate a packet for transmission to the payment instrumentissuer such as an encrypted packet with a random number that can verifythe operation of the chip and the association of the chip with thecustomer's account. Chips may fail for different reasons such as wearingof the contacts through repeated use or other factors.

Payment instruments may also comprise a contactless payment interfacesuch as a near field communications (NFC) payment interface. In someembodiments, the contactless payment interface may comprise a legacymagnetic stripe radio frequency identifier (RFID) tag and, in furtherembodiments, the contactless payment interface may comprise an NFCpayment interface coupled with the chip. Either or both of suchcontactless payment interfaces may include an antenna that is typicallyembedded in the payment instrument and encircles a portion of thepayment instrument. The antenna enables reception of radio signals andfor interacting with a tap type payment interface on a POS terminal andis susceptible to breakage.

Any one or all the payment interfaces can break over time orinstantaneously, causing a customer to experience issues with conductingtransactions with POS terminals via one or more of the paymentinterfaces. Such interactions can develop transaction data descriptiveof issues with a particular payment interface that is transmitted to thepayment instrument issuer in response to completion of a transaction.Other customers may, however, stop using the faulty payment interface orstop using the payment instrument with the faulty interface withoutrepeatedly trying to process the transaction with the faulty ormalfunctioning payment interface. In either case, the customer mayportray the problem with the malfunctioning payment interface through adeviation from a normal or typical behavioral pattern that is exhibitedas a pattern in the transactions conducted by the customer.

To illustrate, when one payment interface stops working, a customer mayopt to use another credit card instead of going through the process ofhaving their credit card replaced. For instance, assume a customer hastwo credit cards, A and B, where credit card A is their primary card. Ifthe tap interface for credit card A stops working, the customer may notknow why the payment interface does not work and may just decide to usecredit card B for small purchases (eligible for tap payments), insteadof using the chip or swipe functionality of credit card A. This willresult in customer dissatisfaction with the credit card, a correspondingreduction in purchase volume for credit card A, and the potential forcredit card A to lose its status as first-in-wallet, which is apreferred card status.

Contemporary systems may determine a payment interface failure at thepoint-of-sale terminal or other credit card terminal, but thepoint-of-sale terminal may be unable to distinguish between a failure inthe payment interface of the payment instrument and a failure in thepayment interface of the point-of-sale terminal. If the customer ormerchant continues to attempt to use the same payment interfaceunsuccessfully, the point-of-sale terminal may instruct the customer ormerchant to initiate a fallback transaction. For instance, thepoint-of-sale terminal may instruct the customer or merchant to attemptto process the transaction with the chip if the tap repeatedly fails andwith the magnetic stripe if the chip repeatedly fails. An increase infallbacks, particularly fallbacks related to a faulty chip can beindicative of a chip malfunction and fallbacks related to chips aretypically declined. Thus, fallbacks can be a good indicator that apayment interface of a payment instrument is malfunctioning. POSterminals may report fallbacks and declines in authorization data, whichis a type of transaction data as discussed herein.

While the POS terminal may report fallback transactions to the creditcard issuer, such information does not distinguish between a malfunctionof a payment interface of a POS terminal and a malfunction of a paymentinterface of a credit card. Moreover, customers that encounter a problemwith, e.g., a tap interface to pay for, e.g., a coffee, may decide toswitch to a second credit card and process the transaction with a tap ofthe second credit card. In such situations, the credit card issuer doesnot even receive an indication of a fallback transaction. The creditcard issuer of the card with the malfunctioning tap interface mayreceive no indication that the customer attempted to use the creditcard.

Embodiments may implement a model with machine learning algorithms orother mathematical algorithms, such as a neural network or otherprediction, classification, or clustering algorithm, to identify changesin transaction behavior by a customer that may indicate a paymentinstrument, such as a credit card, has one or more malfunctioningpayment interfaces that occasionally or permanently cause a failure tocomplete a payment via one or more transaction modes. Some embodimentsimplement models that use the information provided through transactionrecords to deterministically identify a failure in a single payment mode(for instance, repeated failures of one payment interface). Otherembodiments use machine learning to process this information tostrengthen the recognition of payment interface failures.

To prepare a model for determination of a probability of a malfunctionof a payment interface, malfunction logic circuitry may train the modelusing past or historical transaction data from multiple customers. Bytraining the model with data from multiple customers, the model canassess the likelihood, or probability, that the customer's behavior, interms of conducting transactions, indicates a payment interfacemalfunction, that the payment instrument will malfunction in futuretransactions, or the like.

Some embodiments may select transaction data from various customershistorical transaction data based on availability to the paymentinstrument issuer. For instance, the payment instrument issuer may haveaccess to transaction data such as authorization data provided by a POSterminal for authorization of a transaction, internal information abouta request to replace a payment instrument, and/or the like. Theauthorization data may include, e.g., the date and time of theauthorization, a POS entry mode (or transaction mode) used forauthorization, an indication regarding the use of a fallback transactionfor the authorization, and/or the like. The internal information mayinclude, for instance, information about a request type or reason forrequesting the last payment instrument issued on an account, the date ofthe last payment instrument request on the account, and/or the like.

Many embodiments split the transaction data into training data andtesting data. The model may train with the training data and test theaccuracy of the probability output by calculating probabilities ininference mode based on the testing data. The transaction data mayinclude historical transaction data (also referred to as the purchasehistory) from a database of actual, modified, and/or synthesizedtransaction data. In several embodiments, the transaction data is fromor is based on historical transaction data from multiple customers. Infurther embodiments, the transaction data is selected based on acorrelation between the data and a customer request for issuance of anew payment instrument. For example, a customer whom typically uses thetap interface on a first credit card may call to request reissuance ofthe first credit card after recognition that the tap interface is notbeing accepted by a POS of a retailer at which the customer typicallyshops.

The transaction data for the customer for, e.g., the last two weeksprior to the customer's request to reissue the first credit card mayshow a change in a transaction behavior from typical, repetitivetransaction behavior to transaction behavior influenced by a failing orfailed payment interface. For example, the customer may display abehavioral pattern frequenting one or more specific retailers andconducting transactions with the tap interface of the customer's firstcredit card. In some instances, the customer may stop using the tapinterface at those retailers and/or the customer may begin to use adifferent payment interface of the same payment instrument such as thechip or magnetic stripe of the first credit card.

In further embodiments, the customer may indicate that the customer'sreason for requesting the new card is that the tap interface does notseem to work. In such embodiments, the payment instrument issuer maynote this request and the reason for the request as internal transactiondata associated with the customer. For instance, the payment instrumentissuer may store the reason that the customer offers for requesting thereissuance of the first credit card as an indicator of a suspectedmalfunction of the credit card or an indicator that the reason offeredby the customer does not fall within other categories of reasons forrequesting reissuance.

In many embodiments, the models such as neural networks or othermathematical models may pretrain on a server of, e.g., a paymentinstrument issuer, with function approximation, logistic regressionanalysis, or classification to output a probability that a time seriesof transactions for a customer indicates that one or more paymentinterfaces of the payment instrument are malfunctioning. In general, thetraining data may train the model to identify a pattern as being apattern associated with a malfunctioning payment interface by trainingthe model with multiple time series of transactions that led up to acustomer requesting the reissuance of a payment instrument. The modelsmay, through various different methods, learn to detect a behavioralpattern through training the models with many time series oftransactions, determining an error between the output and an expectedoutput, and back propagating the error through the layers of the modelto adjust weights and biases associated with compute nodes within thelayers.

In some embodiments, a model may train with training data that includestypical patterns of behavior of customers, so the model can determine aprobability that the customer's deviation from the typical behavioralpatterns indicate that a payment interface of the customer's paymentinstrument is malfunctioning. In some embodiments, a model may trainwith training data that includes typical behavioral patterns that leadto a request for reissuance of a payment instrument by customers, so themodel can predict, classify, or cluster to detect a behavioral patternof a customer with a probability that the behavioral pattern is similarto or the same as one of the behavioral patterns that typically leads tothe customer requesting reissuance of a payment instrument. In someembodiments, a model may train with training data that includessynthesized transaction data to display patterns of behavior that areconsidered to indicate that a payment interface of a payment instrumentis malfunctioning, so the model can predict, classify, or cluster todetect a behavioral pattern of a customer with a probability that thebehavioral pattern is similar to or the same as one or more of thesynthesized behavioral patterns. Through, e.g., supervised training, theprobabilities of each of these types of training can be improved.Furthermore, several embodiments combine one or more of or all the abovetypes of training data to train a model.

Note that while the model is trained to detect a pattern of transactionsthat represents a probability that one or more payment interfaces of thepayment instrument are malfunctioning, malfunction detection logiccircuitry may detect a malfunction based on a determination by the modelof the error associated with predicting a transaction conducted by thecustomer or classifying the transaction conducted by the customer. Inother words, the model may learn a behavioral pattern of the customerfrom historical transaction data and determine the probability that apattern of one or more recent transactions represent a malfunctioningpayment instrument interface based on deviation from that behavioralpattern. When data for a transaction is input at the input layer of themodel that matches a predicted transaction based on the learnedbehavioral pattern, the model may output an error that is small toindicate a low probability that the transaction indicates amalfunctioning payment instrument interface because the transactionclosely matches a predicted transaction or a transaction classification.On the other hand, when data for a transaction is provided at the inputlayer of the model that is not predicted by the model, the model mayoutput a large error indicating that the transaction does not match apredicted transaction well or a transaction classification well. Thus,the malfunction detection logic circuitry may determine if a transactionis likely indicative of a malfunctioning payment instrument by comparingthe error output by the model to a deviation threshold. For transactionsin which the output error falls below the deviation threshold, themalfunction detection logic circuitry may consider the transaction to bepredictable based on the learned behavioral pattern. For transactions inwhich the output error reaches or exceeds the deviation threshold, themalfunction detection logic circuitry may consider the transaction to beindicative of a malfunctioning payment instrument interface.

Function approximation may involve time series prediction and modelingto generate a probability. A time series is a series of data pointsindexed (or listed or graphed) in time order. As discussed herein, atime series is a sequence of discrete-time transaction data related totransactions conducted at a discrete time of the day, day of the week,week of the month, month of the year, and/or the like, to classify thetransaction data. Some models may perform time series analysis toextract meaningful statistics and other characteristics of thetransaction data to predict a probability of future values beingindicative of a malfunctioning payment interface based on previouslyobserved values.

Classification may involve pattern and sequence recognition, noveltydetection, and the like. For instance, some models may perform sequencelearning such as sequence prediction and sequence recognition toclassify transactions with a probability that a payment interface ismalfunctioning.

Examples of neural networks to output the probability of amalfunctioning payment interface may include a deep neural network, arecurrent neural network, a gradient boosting engine, and/or the like.Examples of mathematical models to output the probability of amalfunctioning payment interface may include stochastic processes.

In many embodiments, the output of a trained model is a probability thatone or more payment interfaces of a payment instrument ismalfunctioning. If the probability meets a certain threshold, thepayment instrument issuer can proactively replace the customer's paymentinstrument, proactively offer to replace the customer's paymentinstrument, and/or proactively inquire about the customer's experiencewith the functionality of one or more of the payment interfaces of thepayment instrument.

In response to an output that a payment instrument interface ismalfunctioning, in some embodiments, the model may initiate a reissuanceof the payment instrument. In further embodiments, the model mayinitiate a request for reissuance of the payment instrument, cause arequest to be sent for input from the customer about the operation ofthe payment instrument, or cause a request to be sent for input from thecustomer about the reissuance of the payment instrument.

Note that logic circuitry refers to circuitry that implements logic withdiscrete components and/or integrated circuits; circuitry withprocessing capabilities to perform logic at least partially in the formof code along with the code; circuitry with buffers, other storagemedia, and/or other storage capabilities to store code along with thecode; and/or a combination thereof.

Many embodiments pretrain the model with multiple sets of transactions,each set including transaction data for a sequence or time series oftransactions by a customer. In several embodiments, the model isinitially pretrained with sets of transactions from multiple customersto train the model about common sequences of transactions that mayrepresent common behavioral patterns. Some embodiments select differentsets of transactions from the multiple customers to train the model withtransaction sequences that have different counts of transactions thatmay begin, e.g., at different times, advantageously increasing therobustness of the model's ability to recognize transactions thatcorrespond to behavioral patterns. Some embodiments randomly selecttransactions to generate a time series of transactions with which totrain the model to increase robustness of the training. And someembodiments introduce fuzziness to the values such as transactionvalues, geo-location values, time values, other factors implemented asinputs for the model, and the like, to increase the robustness of thetraining for the model. Introduction of fuzziness to a value intransaction data refers to a modification of a value in the transactiondata where the modification typically changes the value by less than anorder of magnitude.

In some embodiments, one or more instances of the model may monitortransactions of a customer within a window of time, or a time frame,such as several weeks, to identify a malfunctioning payment instrumentinterface within that time frame. In several embodiments, the time framemay be adjusted or tuned over time based on detection of amalfunctioning payment instrument interface, based on customer feedback,and/or based on other factors. In other embodiments, rather thandefining a time frame, the model may monitor a particular count of or anumber of transactions within a time series of a customer. In severalembodiments, the time series of transactions may include selectedtransactions such as only periodic transactions that occur, e.g., on adaily basis, every other day, every few days, once a week, and/or thelike for a particular customer. For example, the time series oftransactions may include “Card present” transactions and may also belimited to transactions that, e.g., occur periodically; during certaindays of the week such as during a week day; during certain times of theday such as prior to work hours, during lunch time frame, or after workhours; and/or the like. In some embodiments, the transactions may onlyinclude repetitive transactions for which a customer typically uses thesame payment instrument interface.

In other embodiments, after pretraining with transaction data frommultiple customers or all customers, the model may be trained, orretrained, with sets of transactions from a specific customer to trainthe model with behavioral patterns of the specific customer and/or totrain the model to detect patterns of transactions that lead to arequest for reissuance of a payment instrument. Similar to thepretraining with the sets of transactions from multiple customers, thepretraining with sets of transactions from the specific customer'spurchase history may include transaction sequences that have differentcounts of transactions, one or more synthesized time series of randomlyselected transactions, and fuzzy values from the customer's purchasehistory. Fuzzy values refer to values in transaction data that aremodified so the values do not reflect the actual purchase information.Such changes to the values in transaction data typically do not exceedan order of magnitude. In several embodiments, a model is pretrained foreach customer, so each customer advantageously has a model, specificallyadapted to the purchasing habits of that specific customer, to monitorthat specific customer's transactions for deviations that are indicativeof a malfunctioning payment interface.

Once the model launches and begins to monitor the transactions for thespecific customer, the model may, in many embodiments, may collect new,transaction data as new training data. In other words, once themalfunction detection logic circuitry confirms or verifies that atransaction is matches a behavioral pattern of the customer and/or isindicative of a payment instrument interface malfunctioning, themalfunction detection logic circuitry may train the model with the newtransaction data, advantageously, teaching the model the contemporaneouspurchasing habits of the specific customer to continuously improve themodel's ability to determine a probability that one or more of thepayment interfaces of the customer's payment instrument ismalfunctioning.

Several embodiments comprise systems with multiple processor cores suchas central servers, access points, and/or stations (STAs) such asmodems, routers, switches, servers, workstations, netbooks, mobiledevices (Laptop, Smart Phone, Tablet, and the like), sensors, meters,controls, instruments, monitors, home or office appliances, Internet ofThings (IoT) gear (watches, glasses, headphones, and the like), and thelike. In various embodiments, these devices relate to specificapplications such as healthcare, home, commercial office and retail,security, and industrial automation and monitoring applications, as wellas vehicle applications (automobiles, self-driving vehicles, airplanes,drones, and the like), and the like.

Turning now to the drawings, FIGS. 1A-C depict embodiments of systemsincluding servers, networks, point-of-sale (POS) terminals, and paymentinstruments to detect a malfunctioning payment interface of a paymentinstrument with multiple payment interfaces. FIG. 1A illustrates anembodiment of a system 1000. The system 1000 may represent a portion ofat least one wireless or wired network 1025 that interconnects server(s)1010 with POS devices 1030 and 1040. The at least one wireless or wirednetwork 1025 may represent any type of network or communications mediumthat can interconnect the server(s) 1010 and the POS devices 1030 and1040, such as a cellular service, a cellular data service, satelliteservice, other wireless communication networks, fiber optic services,other land-based services, and/or the like, along with supportingequipment such as hubs, routers, switches, amplifiers, and/or the like.

In the present embodiment, the server(s) 1010 may represent one or moreservers owned and/or operated by a company that provides services. Insome embodiments, the server(s) 1010 represent more than one companythat provides services. For example, a first set of one or moreserver(s) 1010 may provide services that includes pretraining a modelsuch as a neural network 1017 of a malfunction detection logic circuitry1015 with transaction data from more than one customer. The first set ofserver(s) 1010 may comprise anonymized transaction data that, in manyembodiments, comprise behavioral patterns, or transaction patterns, ofmultiple customers including behavioral patterns indicative of amalfunctioning payment interface and/or indicative of functioningpayment interfaces. In some embodiments, the transaction data is notanonymized but the neural network 1017 may not require transaction datathat would identify a particular customer.

In other embodiments, the model may be a mathematical model rather thana neural network. The mathematical model may comprise machine learningalgorithms to learn behavioral patterns of customers that are indicativeof a malfunctioning payment interface and/or behavioral patterns thatare not indicative of a malfunctioning payment interface. In furtherembodiments, the mathematical model may comprise stochastic processesthat include statistical algorithms to determine a probability based ona time series of transactions.

The first set of server(s) 1010 may pretrain the neural network 1017 tocluster, classify, and/or predict transactions to determine aprobability that a payment interface of a payment instrument ismalfunctioning. The pretraining may involve training the neural network1017 with sets of transactions from multiple customers. The sets oftransaction data may be split into sets of training data and sets oftesting data. The model can train with the sets of training data andthen tests can run on the model with the sets of testing data todetermine the accuracy of the probabilities output by the neural network1017.

Each set of transaction data represented in the training data andtesting data may comprise a sequence of transactions that occur in aseries such as a time series or time sequence. Furthermore, someembodiments may selectively include transactions for training byremoving or otherwise not including other transactions in a time series.Many embodiments, for example, include only “Card Present” transactionssince the “Card Present” transactions include transactions for which aphysical payment instrument performs the transaction. For example, POSdevices 1030 and 1040 may represent terminals that two differentretailers use to process transactions with payment instruments such ascredit cards. POS device 1030 may represent a POS terminal at a gas pumpof a gas station and the payment interface 1037 may be a paymentinterface of the POS terminal 1030. The payment interface 1037 mayrepresent one or more payment interfaces such as a tap interface, a chipinterface, and/or a magnetic stripe interface for processingtransactions. To perform a transaction with the tap interface, chipinterface, or magnetic stripe interface, the payment instrument isphysically presented to the payment interface 1037 and, thus, thetransaction is considered a “Card Present” transaction. Similarly, thePOS device 1040 may comprise a payment interface 1047 to perform “CardPresent” transactions for a grocery store and may require a paymentinstrument to be physically present to perform a transaction.

In further embodiments, the training data and testing data may onlyinclude transactions within a period of time prior to a request forreissuance of the customer's payment instrument. Such embodiments mayinclude a setting or a heuristic or machine learning algorithm to selectthe time frame. Other embodiments may randomly or pseudo-randomly selectthe time frame within predefined parameters such as randomly orpseudo-randomly selecting a time frame that is a minimum of one day anda maximum of two months. In some embodiments, the time frame may bebetween a minimum of multiple days, such as 2 days, 5 days, or 10 days,and a maximum of multiple days, such as 21 days, 45 days, or 90 days.

Some embodiments may selectively train and test the neural network 1017with transaction data that conveys a repetitive behavioral pattern (ortransaction pattern). For instance, customers may purchase gasoline fora vehicle periodically (e.g. once a week) and may always orpredominantly perform the transaction to purchase the gasoline with thetap of a payment instrument. Similarly, the customers may typicallypurchase lunch (e.g., three to four times a week) with the magneticstripe of the payment instrument and the customers may purchasegroceries (e.g., once a week) with the chip of the payment instrument.Thus, the neural network 1017 can train with such transactions so theneural network 1017 can output a probability of a malfunction of apayment interface of a customer's payment instrument based on adeviation from typical transaction pattern for, e.g., Card Presenttransactions. For example, if the customer typically purchases groceriesonce a week with the chip interface of the payment instrument, thispattern may be apparent in the historical transaction data for thecustomer over many months with only minor deviations when that customeris on vacation or on a business trip. Thus, the neural network 1017 canlearn the repetitive or periodic behavior with respect to conductingtransactions at the grocery store. Thereafter, if the transaction dataincludes one or more fallback indicators that indicate fallbacktransactions and possibly one or more declination indicators to indicatedeclines due to a failure to authorize transactions via the chipinterface, the neural network 1017 can compare this change in thetransaction data to the repetitive pattern from the customer'shistorical transaction data to determine a high probability that thechip interface of the payment instrument is malfunctioning. Furthermore,if the transaction data exhibits a decrease in the frequency of use ofthe payment instrument in conjunction with one or more fallbackindicators and/or one or more declination indicators, the model maydetermine a high probability that the chip interface of the paymentinstrument is malfunctioning. Still further, if the transaction dataexhibits a sudden and/or significant decrease in the frequency of use ofthe payment instrument, the model may determine a high probability thatthe chip interface of the payment instrument is malfunctioning.Significant may comprise, for instance, 10 percent or more decrease inthe use of the payment instrument. In many embodiments, the actualpercentage may be based on learned behavioral patterns based on trainingof the model with transaction data from multiple customers.

In some situations, the customer may switch to a different paymentinstrument, so the transaction data may exhibit a repetitive pattern ofgrocery store purchases with the chip interface followed by fallbackindicators and/or declination indicators relating to a failure toauthorize transactions via a chip interface, followed by an absence inchip interface transactions at the grocery store. The greater thedeviation that the neural network 1017 computes based on training tolearn typical behavioral patterns, the larger the probability output bythe neural network 1017.

Further embodiments may also train with training data that combines setsof transactions that exhibit typical behavioral patterns with sets oftransactions associated with a payment interface malfunction. In otherwords, the neural network 1017 may train with the repetitive transactionpatterns of the customers and also with transaction patterns related toa failure or malfunction of the payment interface. Still otherembodiments may only train the neural network 1017 with behavioralpatterns, or spending behavior, associated with a payment interfacemalfunction.

After pretraining, the neural network 1017 with the transaction datafrom multiple customers, the first set of the server(s) 1010 or a secondset of one or more server(s) 1010 may execute one or more instances ofthe neural network 1017 such as the neural networks 1018 and 1019 toperiodically monitor transactions of customers in parallel over a timeframe or for a certain count of transactions. For example, themalfunction detection logic circuitry 1015 may receive transaction datafor a recent transaction conducted by a customer, retrieve or otherwisereceive historical transaction data from a customer historicaltransaction database 1020 for the customer, and input the transactiondata and the historical transaction data at the input layer of theneural network 1017. The customer historical transaction database 1020may be a database established to include selected transaction data fromeach of the customers or may comprise a database of all recordedtransaction data for multiple customers. In some embodiments, thecustomer historical transaction database 1020 is a distinct storagesystem from the server(s) 1010 and, in other embodiments, the customerhistorical transaction database 1020 is part of the server(s) 1010.

The neural network 1017 may, based on training of the neural network1017, determine and output a probability that the transaction data andhistorical transaction data includes usage of payment interfaces of apayment instrument that is indicative of one or more malfunctioningpayment interfaces. For example, the customer exhibits a behavioralpattern in which the customer purchases gas once a week with the tapinterface of the payment instrument. The neural network 1017 mayreceive, as input tensors, the transaction data that includes anindication of a fallback mode from the tap interface to the chipinterface, and thereafter in the time series, no further purchasesgasoline over several weeks with the payment instrument. In suchembodiments, the neural network 1017 may calculate a probability thatthe tap interface of the payment instrument malfunctioned or aprobability that at least one of the payment interfaces malfunctionedbased on the deviation by the customer from behavioral patterns of thecustomer or of multiple customers as learned from pretraining.

In other embodiments, after pretraining the neural network 1017 with thetransaction data from multiple customers, the first set of the server(s)1010 or a second set of one or more server(s) 1010 may continue to trainor retrain one or more instances of the neural network 1017 withpurchase history of one or more specific customers. For example, someembodiments fully train the neural network 1017 with the transactiondata from multiple customers prior to training an instance of the neuralnetwork 1017 with purchase history of a specific customer. Someembodiments fully train the neural network 1017 with the transactiondata from multiple customers prior to training an instance of the neuralnetwork 1017 with purchase history of a subset of customers that exhibitsimilar behavioral patterns while the payment instrument is notmalfunctioning. Other embodiments may not fully train the neural network1017 prior to provision of an instance to train with purchase history ofa specific customer or a subset of customers. Note that fully training aneural network such as the neural network 1017 may involve training theneural network 1017 with sufficient samples of training data for theneural network 1017 to converge on solutions for, e.g., multiplepredicted transactions or multiple classifications based on differentinitial conditions or initial states of the neural network 1017.

The neural networks 1018 and 1019, in such embodiments, may representinstances of the neural network 1017 retrained for specific customers ora subset of customers that exhibit similar behavioral patterns while thepayment instrument is not malfunctioning. In such embodiments, ratherthan providing, as input to the neural network 1018, historicaltransaction data for the specific customer along with transaction datafor a recent transaction, the neural network 1018 may be designated forthe specific customer and may only receive, as input, transaction datafor that specific customer as the customer conducts transactions.

In several embodiments, one or more the server(s) 1010 may performmalfunction detection with the neural networks 1018 and 1019. Forexample, when a first customer completes a transaction such aspurchasing gas, the malfunction detection logic circuitry 1015 may applytransaction data that describes the purchase as a tensor to the inputlayer of the neural network 1018. The neural network 1018 may operate ininference mode and output an indication of an error or probabilityassociated with the purchase. The probability may represent a differencebetween the purchase of the gas and a predicted range of transactionsthat the neural network 1018 determines based on the pretraining and/orthe continued training with new transactions for this customer. If thecustomer buys the gas at the same gas station for about the same amount,at about the same time, on about the same day of the week that thecustomer normally purchases gas for a vehicle with the same paymentinterface such as the tap interface, the probability output from theneural network 1018 will likely be very small if not nil. On the otherhand, if one or more of these factors deviate significantly frombehavioral patterns in the customer's purchase history and/or from thesequences of transactions learned from training on transaction data frommultiple customers, the probability output from the neural network 1018may be large. In many embodiments, the purchase history may exhibitbehavioral patterns of customer such as the payment interface thatcustomers typically use to conduct various types of transactions andtypical time periods between purchases of various goods or services.

Preferences by a company associated with the transactions, a companyassociated with the server(s) 1010 (if different), and/or the customerassociated with the neural network 1018 may establish or factor into adetermination of a probability threshold. The malfunction detectionlogic circuitry 1015 may compare the probability threshold to the erroror probability output by the neural network 1018 in response to thepurchase to determine if proactive action should occur in response tothe probability output. For example, in response to a probability outputthat reaches or exceeds a probability threshold, the malfunctiondetection logic circuitry 1015 may determine that one or more of thepayment interfaces of the customer's payment instrument has failed orotherwise malfunctioned and, in response, issue, e.g., a request for anew payment instrument for the customer, a request for the customer toprovide input about the functionality of the payment instrument, and/ora request to ask the customer if the customer wants a new paymentinstrument issued.

In many embodiments, the malfunction detection logic circuitry 1015 maycontinue to collect training data and train the neural network 1017 andinstances of the neural network 1017 based on customer transactions frommultiple customers. In other embodiments, the malfunction detectionlogic circuitry 1015 may continue to collect training data and train theneural network 1017 and instances of the neural network 1017 based oncustomer transactions of specific customers.

FIG. 1B depicts an embodiment for a payment instrument 1100 such as acredit card with multiple payment interfaces to conduct transactionswith POS terminals such as the POS devices 1030 and 1040 shown in FIG.1A. The payment instrument 1100 may be plastic, metal, or othermaterial. In the present embodiment, the payment instrument 1100comprises an antenna 1110 coupled with a wireless communicationinterface 1120, a legacy magnetic stripe 1130 coupled with the wirelesscommunication interface 1120, a chip coupled with the wirelesscommunication interface 1120, and a magnetic stripe 1150. The antenna1110 may comprise a conductive material that forms a loop around atleast a portion of the payment instrument 1100. If the paymentinstrument 1100 comprises a conductive material, a non-conductivematerial may isolate the loop of the antenna 1110 from conductivematerials in the payment instrument 1100.

The wireless communication interface 1120, or contactless interface, maycomprise a near-field communications interface and may also be referredto as the tap interface. The wireless communication interface 1120 maybe a passive device that draws power from a transmitter to activate,receive a transmission, and transmit a response. In other embodiments,the wireless communication interface 1120 may comprise abattery-powered, active interface or a battery-assisted, passiveinterface.

The legacy magnetic stripe 1130 may comprise a radio frequencyidentification (RFID) tag. The tag may comprise an integrated circuitcoupled with the wireless communication interface 1120 and the antenna1110 to respond to a transmitter of a POS terminal, such as the POSdevice 1030 shown in FIG. 1A, with information such as the informationstored on the magnetic stripe 1150. In some embodiments, the legacymagnetic stripe 1130 includes security protocols such as shorttransmission ranges, rolling codes, challenge-response authentication,and/or cryptographically coded responses.

The legacy magnetic stripe 1130 may couple with the wirelesscommunication interface 1120 to conduct a transaction via a contactlessinterface, or tap interface, of the payment instrument 1100. In someembodiments, the legacy magnetic stripe 1130 may comprise the onlydevice to conduct transactions through a tap interface of the paymentinstrument 1100. In other embodiments, the legacy magnetic stripe 1130may comprise one of multiple devices that can conduct transactionsthrough the tap interface of the payment instrument 1100.

The chip 1140 may comprise a microchip, other processor, and/orintegrated circuit including memory. In some embodiments, the chip 1140may comprise contacts and may be a contacted interface for the paymentinstrument 1100 that is often referred to as the chip interface. Infurther embodiments, the chip 1140 may couple with the wirelesscommunication interface and comprise a contactless interface, or tapinterface, of the payment instrument 1100. In some embodiment, the chip1140, wireless communication interface 1120, and antenna 1110 form theonly tap interface for the payment instrument 1100. In otherembodiments, the chip 1140 may comprise part of one of the tapinterfaces for the payment instrument 1100.

Some embodiments of the payment instrument 1100 may have two paymentinterfaces such as a combination of the chip 1140 as a contactedinterface and the magnetic stripe 1150 as a contacted interface. Otherembodiments may comprise three payment interfaces such as a combinationof the chip 1140 as a contacted interface, the magnetic stripe 1150 as acontacted interface, and the legacy magnetic stripe 1130 as acontactless interface. Further embodiments may have any number ofpayment interfaces such as the payment interfaces discussed herein orother payment interfaces.

FIG. 1C depicts an embodiment for an apparatus 1200 such as one of theserver(s) 1010 shown in FIG. 1A. The apparatus 1200 may be a computer inthe form of a smart phone, a tablet, a notebook, a desktop computer, aworkstation, or a server. The apparatus 1200 can combine with anysuitable embodiment of the systems, devices, and methods disclosedherein. The apparatus 1200 can include processor(s) 1210, anon-transitory storage medium 1220, communication interface 1230, and adisplay 1235. The processor(s) 1210 may comprise one or more processors,such as a programmable processor (e.g., a central processing unit(CPU)). The processor(s) 1210 may comprise processing circuitry toimplement malfunction detection logic circuitry 1215 such as themalfunction detection logic circuitry 1015, 1018, or 1019 in FIG. 1A.

The processor(s) 1210 may operatively couple with a non-transitorystorage medium 1220. The non-transitory storage medium 1220 may storelogic, code, and/or program instructions executable by the processor(s)1210 for performing one or more instructions including the malfunctiondetection logic circuitry 1225. The non-transitory storage medium 1220may comprise one or more memory units (e.g., removable media or externalstorage such as a secure digital (SD) card, random-access memory (RAM),a flash drive, a hard drive, and/or the like). The memory units of thenon-transitory storage medium 1220 can store logic, code and/or programinstructions executable by the processor(s) 1210 to perform any suitableembodiment of the methods described herein. For example, theprocessor(s) 1210 may execute instructions such as instructions ofmalfunction detection logic circuitry 1225 causing one or moreprocessors of the processor(s) 1210 represented by the malfunctiondetection logic circuitry 1215 to perform an inference computation, by aneural network of the malfunction detection logic circuitry 1215 basedon transaction data. The inference computation may determine aprobability that a behavioral pattern exhibited by transactions of acustomer indicate a malfunction of one or more payment interfaces of thecustomer's payment instrument. The neural network may determine theprobability based on pretraining of the neural network to cluster,predict, or classify a set of transactions conducted by the customer.The pretraining of the neural network to predict malfunctions of paymentinterfaces of a customer's payment instrument may be based on a purchasehistory of multiple customers.

Once the neural network determines the processes a series oftransactions for a customer and determines a probability based on adeviation from a typical behavioral pattern of customers and/or abehavioral pattern associated with a request by a customer to reissue apayment instrument, the malfunction detection logic circuitry 1215 maydetermine whether the probability exceeds a probability threshold. Theprobability threshold may represent a threshold chosen, calculated, orotherwise determined to indicate a deviation from a behavioral patternthat indicates that a payment interface of the customer's paymentinstrument is likely malfunctioning. In response to a determination thata payment interface is malfunctioning, the malfunction detection logiccircuitry 1215 may cause communication of a request to reissue a paymentinstrument to the customer, cause communication of an inquiry to thecustomer, cause a message to display on a display 1235, and/or the like.

The processor(s) 1210 may couple to a communication interface 1230 totransmit and/or receive data from one or more external devices (e.g., aterminal, display device, a smart phone, a tablet, a server, or otherremote device). The communication interface 1230 includes circuitry totransmit and receive communications through a wired and/or wirelesscommunication medium such as an Ethernet interface, a wireless fidelity(Wi-Fi) interface, a cellular data interface, and/or the like. In someembodiments, the communication interface 1230 may implement logic suchas code in a baseband processor to interact with a physical layer deviceto transmit and receive wireless communications such as transaction datafrom a server or a data storage system. For example, the communicationinterface 1230 may implement one or more of local area networks (LAN),wide area networks (WAN), infrared, radio, Wi-Fi, point-to-point (P2P)networks, telecommunication networks, cloud communication, and the like.

The processor(s) 1210 may couple to a display 1235 to display a messageor notification via, graphics, video, text, and/or the like. In someembodiments, the display 1235 may comprise a display on a terminal, adisplay device, a smart phone, a tablet, a server, or a remote device.

FIG. 1D depicts an embodiment of a neural network 1500 of a malfunctiondetection logic circuitry, such as malfunction detection logic circuitry1015 illustrated in FIG. 1A and the malfunction detection logiccircuitry 1215 and 1225 illustrated in FIG. 1B. FIG. 1D depicts anembodiment of stages of the neural network (NN) 1500 such as a recurrentneural network (RNN).

An RNN is a class of artificial neural network where connections betweennodes form a directed graph along a sequence. This allows the RNN toexhibit dynamic temporal behavior for a time sequence. RNNs can usetheir internal state (memory) to process sequences of inputs and canhave a finite impulse structure or an infinite impulse structure. Afinite impulse recurrent network is a directed acyclic graph that can beunrolled and replaced with a strictly feedforward neural network, whilean infinite impulse recurrent network is a directed cyclic graph thatcannot be unrolled. A feedforward neural network is a neural network inwhich the output of each layer is the input of a subsequent layer in theneural network rather than having a recursive loop at each layer.

The neural network 1500 illustrates an embodiment of a feedforwardneural network, but other embodiments may comprise other RNNs or othertypes of NNs. The neural network 1500 comprises an input layer 1510, andthree or more layers 1520 and 1530 through 1540. The input layer 1510may comprise input data that is training data for the neural network1500 or new transaction data to evaluate. The input layer 1510 mayprovide the transaction data in the form of tensor data to the layer1520. The transaction data may comprise transaction information, whichis data related to a purchase by a customer. The transaction data mayinclude information indicating whether the transaction is a “CardPresent” transaction or a “Card Not Present” transaction, locationinformation, value information, transaction type information, timeinformation, and/or the like. For instance, the location information mayinclude coordinates, a distance from an address associated with acustomer, an address, a city, a state, a zip code, a map quadrant,and/or the like. The value information may include a purchase price, avalue on hold, a sub-total, a tax, and/or the like. The transaction typeinformation may include an indication of a general type and/or subtypeof purchase such as a retail purchase, a gas purchase, a vehiclemaintenance fee, a travel expense, a tax, a government fee, and/or thelike. And the time information may include a time, a day, a month, ayear, a season, a quarter, and/or the like.

In many embodiments, the input layer 1510 is not modified bybackpropagation. The layer 1520 may compute an output and pass theoutput to the layer 1530. Layer 1530 may determine an output based onthe input from layer 1520 and pass the output to the next layer and soon until the layer 1540 receives the output of the second to last layerin the neural network 1500. For embodiments with RNNs that are notfeedforward (not shown), each layer may recursively compute an outputbased on the output of the layer being fed back into the same layer asan input.

The layer 1540 may generate an output and pass the output to anobjective function logic circuitry 1550. The objective function logiccircuitry 1550 may determine errors in the output from the layer 1540based on an objective function such as a comparison of the expectedoutput against the actual output. For instance, the expected output maybe paired with the input in the training data supplied for the neuralnetwork 1500 for supervised training. In the present embodiment, duringtraining, the probability output of the objective function logiccircuitry 1550 should be less than a probability threshold if thetraining data is known, selected, or synthesized to represent typicalbehavioral patterns of customers using one or more payment interfaces ofa payment instrument. On the other hand, for training data known,selected, or synthesized to represent behavioral patterns indicative ofcustomer's using or discontinuing use of a payment instrument with amalfunctioning payment interface, the probability output of theobjective function logic circuitry 1550 should be greater than aprobability threshold. When operating in inference mode, the malfunctiondetection logic circuitry, such as the malfunction detection logiccircuitry 1215 shown in FIG. 1C, may compare the output of the objectivefunction logic circuitry 1550 against the probability threshold todetermine if the probability indicates a malfunctioning paymentinterface.

During the training mode, the objective function logic circuitry 1550may output errors to backpropagation logic circuitry 1555 tobackpropagate the errors through the neural network 1500. For instance,the objective function logic circuitry 1550 may output the errors in theform of a gradient of the objective function with respect to theparameters of the neural network 1500.

The backpropagation logic circuitry 1555 may propagate the gradient ofthe objective function from the top-most layer, layer 1540, to thebottom-most layer, layer 1520 using the chain rule. The chain rule is aformula for computing the derivative of the composition of two or morefunctions. That is, if f and g are functions, then the chain ruleexpresses the derivative of their composition f ∘ g (the function whichmaps x to f(g(x))) in terms of the derivatives of f and g. After theobjective function logic circuitry 1550 computes the errors,backpropagation logic circuitry 1555 backpropagates the errors. Thebackpropagation is illustrated with the dashed arrows.

FIG. 2 depicts an embodiment of a malfunction detection logic circuitry2000 such as the malfunction detection logic circuitry 1015 in FIG. 1A.The malfunction detection logic circuitry 2000 may perform one or moreoperations to train a neural network 2010, such as the neural network1500 illustrated in FIG. 1D, to determine a probability that a paymentinterface of a customer's payment instrument is malfunctioning. In otherembodiments, a mathematical model may be implemented instead of a neuralnetwork.

The malfunction detection logic circuitry 2000 may comprise logiccircuitry such as the neural network 2010, a pretrainer 2015, amalfunction determiner 2030, and a trainer 2040. The neural network 2010may comprise one or more recurrent neural networks, a gradient boostingengine, a logistic regression algorithm or analysis model, and/or thelike, to determine a probability that transaction data of a series oftransactions for a customer indicates that the customer's paymentinstrument has a malfunctioning payment interface. In many embodiments,the neural network 2010 may determine a probability based on pretrainingwith sets of transactions from purchase histories of multiple customers.The neural network 2010 may output a probability or error to themalfunction determiner 2030 in response to an input of transaction data2005 from a customer and historical transaction data 2007 for thatcustomer from a historical transaction database 2090, or an input oftraining data or testing data from the pretrainer 2015.

Prior to operation in inference mode, the malfunction detection logiccircuitry 2000 may operate the neural network 2010 in training mode andtrain the neural network 2010 with training data from the pretrainer2015. The pretrainer 2015 may include multiple customer purchase historydata 2022 and new transaction data for training 2026. The multiplecustomer purchase history data 2022 may include transaction data frommultiple customers. In some embodiments, the multiple customer purchasehistory data 2022 is anonymized and/or at least a portion of the data isencrypted. The anonymized data may include transaction data that doesnot have data to identify a customer and may even have modifiedtransaction data that does not accurately describe the transactions bythe customer but does represent the transactions closely enough fortraining the neural network 2010.

In many embodiments, the malfunction detection logic circuitry 2000 mayfirst pretrain the neural network 2010 with the multiple customerpurchase history data 2022. In other embodiments, the malfunctiondetection logic circuitry 2000 may pretrain the neural network 2010 withsynthesized transaction data.

The trainer 2040 may repeatedly select sets of transaction data from themultiple customer purchase history data 2022 for training. Each set mayinclude a sequence or time series of transaction data from, e.g., arandomly selected customer or a customer selected based on a behavioralpattern exhibited in the customer's transaction history. For instance, acustomer's transaction data may be selected based on the inclusion ofone or more requests by the customer for reissuance of the paymentinstrument. In some embodiments, the customer's transaction data may beselected based on typical behavioral patterns exhibited in thecustomer's transaction data. Furthermore, the sets may have differentcounts or numbers of transactions to, advantageously, increase therobustness of the training.

The trainer 2040 comprises logic circuitry may improve the training byoccasionally or periodically modifying the transaction data from thepretrainer 2015. In the present embodiment, the trainer 2040 comprisesrandom 2042 and fuzzy 2044. The random 2042 logic circuitry may formrandom sets of transactions from the multiple customer purchase historydata 2022. For example, the random 2042 logic circuitry may randomlychoose transactions from different customers to form a set oftransactions as a sequence or time series. In some embodiments, therandom selection of transactions may be limited by one or more rulessuch as a rule the prevents purchases within a short or unrealisticperiod of time, purchases within a specified time period, or multiplepurchases of certain types within a specified time period. For instance,if a first purchase in a series is 20 miles from a second purchase andboth are ‘Card Present’ purchases, a rule for the random logic mayprevent the random selection of such purchases within a short orunrealistic period of time such as one minute. Note that the short orunrealistic period of time is defined in the context of the distancebetween the locations of the purchases.

The trainer 2040 may also comprise fuzzy 2044 logic circuitry. The fuzzy2044 logic circuitry may modify values of the transaction data from thepretrainer 2015. For instance, the fuzzy 2044 logic circuitry may makesmall changes to locations of purchases such as moving the locationacross the street, values of transactions such as increasing ordecreasing the value by 10% to 20%, modifying the time of thetransaction, modifying the day of the transaction, and/or the like.Slight modifications to values of transaction data can, advantageously,improve the robustness of the neural network 2010. In severalembodiments, the fuzzy 2044 logic circuitry may modify valuesoccasionally or periodically. For instance, some embodiments may modifyone value of one percent of the transactions received from thepretrainer 2015. Other embodiments may modify multiple values in fivepercent of the transactions. The frequency of such modifications maydepend on design parameters of the neural network 2010.

A backprop 2046 logic circuitry of the trainer 2040 may train the neuralnetwork 2010 by backward propagation of the error that is output by theneural network 2010 in response to the training data. The error mayrepresent a difference between a probability output from the neuralnetwork 2010 and an expected probability. Backward propagation of theerror may adjust weights and biases in the layers of the neural network2010 to reduce the error. The backward propagation, often referred to asbackprop or backpropagation, of the error may effectively adjust therange of, e.g., predicted transactions responsive to the transactiondata that caused the neural network 2010 to output the error.

After pretraining the neural network 2010 with the multiple customerpurchase history data 2022, the malfunction detection logic circuitry2000 may create multiple instances 2012 of the neural network 2010. Insome embodiments, the malfunction detection logic circuitry 2000 maycreate one of the instances 2012 for every customer. In otherembodiments, the malfunction detection logic circuitry 2000 may createone of the instances 2012 for every customer within a group of customersthat exhibit similar behavioral patterns, or transaction patterns, inuse of payment interfaces for transactions. The malfunction detectionlogic circuitry 2000 may select the group of customers based on a listof customers provided to the malfunction detection logic circuitry 2000and/or based on other criteria.

Once the malfunction detection logic circuitry 2000 trains the neuralnetwork 2010, an instance of the neural network 2010 can performmalfunction detection for the specific customer and, in severalembodiments, continue to train with new, transactions completed by thecustomer and/or multiple customers. When the specific customer conductsa transaction, the vendor may transmit information related to thattransaction to the payment instrument issuer. The payment instrumentissuer may comprise a server to perform malfunction detection based onthe instance of the neural network 2010. The malfunction detection logiccircuitry 2000 receives the transaction data 2005 for a customer as aninput as well as historical transaction data 2007 for that same customerfrom the historical transaction database 2090 such as the previous fewweeks of transaction data for the customer and provides the input to theinstance of the neural network 2010.

Based on the transaction data 2005 and the historical transaction data2007, the instance of the neural network 2010 may perform malfunctiondetection in inference mode and output a probability to the malfunctiondeterminer 2030. The probability may represent a likelihood that adeviation from learned behavioral patterns of transaction datarepresents a behavioral pattern that is indicative of a malfunctioningpayment interface of the customer's payment instrument.

The malfunction determiner 2030 may determine if the probability outputby the instance of the neural network 2010 indicates a malfunctioningpayment interface of the customer's payment instrument. The malfunctiondeterminer 2030 may comprise logic circuitry to compare 2032 theprobability to a probability threshold, initiate reissuance 2033 of apayment instrument to a customer, communicate 2036 a message with aninquiry about functionality of one or more payment interfaces of thecustomer's payment instrument or about reissuance of the customer'spayment instrument, and display 2038 a notification on a local or remotedisplay.

The compare 2032 logic circuitry may compare the probability to aprobability threshold to determine if the probability meets or exceedsthe probability threshold. If the probability is equal to or greaterthan the probability threshold, the compare 2032 logic circuitry mayoutput an indication to initiate reissuance 2033 logic circuitry, thecommunicate 2036 logic circuitry, and/or the display 2038 logiccircuitry in response to determining that a payment interface of thecustomer's payment instrument is malfunctioning.

The compare 2032 logic circuitry may instruct the trainer 2040 to add2050 the transaction data 2005 and the historical transaction data 2007to the new transaction data for training 2026. In some embodiments, theadd 2050 logic circuitry may add the transaction data 2005 and thehistorical transaction data 2007 to the new transaction data fortraining 2026 after verification of behavioral pattern in thetransaction data by the confirm 2048 logic circuitry. In such cases,confirm 2048 logic circuitry may confirm, through communication with thecustomer or other confirmation, that a payment interface of thecustomer's payment instrument is likely malfunctioning. In otherembodiments, the add 2050 logic circuitry may add the transaction data2005 to the new transaction data for training 2026 upon determinationthat the probability exceeds the probability threshold. In still otherembodiments, the add 2050 logic circuitry may add the transaction data2005 and the historical transaction data 2007 to the new transactiondata for training 2026 upon determination that the probability does notexceed the probability threshold.

FIGS. 3A-E depict flowcharts of embodiments to pretrain a neuralnetwork, perform malfunction detection with a neural network, andcommunicate with a consumer, by malfunction detection logic circuitry,such as the malfunction detection logic circuitry shown in FIGS. 1A, 1Cand 2. FIG. 3A illustrates a flowchart to pre-train a neural network andthen continue to train the neural network. The flowchart starts withpretraining the neural network based on purchase histories of multiplecustomers to train the neural network to recognize patterns oftransactions that represent behavioral patterns of the customers(element 3010). In other words, the malfunction detection logiccircuitry may train the neural network based on multiple sets of a timeseries or sequence of transactions in the form of transaction data toteach the neural network sequences of transactions that exhibit commonor typical behavioral patterns such as behavioral patterns illustrativeof customers using various payment interfaces of a payment instrumentthat is operating properly and/or behavioral patterns of customers using(or ceasing use of) payment instruments when one or more of the paymentinterfaces on those payment instruments are malfunctioning. Suchtransaction data may include past card requests, declines of paymentinstruments or payment interfaces of payment instruments, transactionsprocessed through different transaction modes (different paymentinterfaces), and/or the like. Many embodiments train the neural networkon a server of the payment instrument issuer or a server by a thirdparty to pretrain the neural network.

After pretraining the neural network based on the purchase histories ofmultiple customers, the flowchart may continue to train the neuralnetwork based on the purchase history of specific customers as suchtransaction data is added to training data sets (element 3020). Inseveral embodiments, the malfunction detection logic circuitry may trainan instance of the neural network on a server with transaction data thatis captured while processing transaction data in an inference mode.

FIG. 3B illustrates a flowchart for pretraining. The flowchart beginswith repeatedly training the neural network based on different counts oftransactions from the purchase history of the customer to generate arange of behavioral patterns of transaction data that the neural networkwill recognize as typical or as representative of a malfunctioningpayment interface (element 3110). In other words, the malfunctiondetection logic circuitry may train the neural network with multipledifferent sets of transactions from the purchase histories of customersand the multiple different sets will not all have the same number oftransactions.

FIG. 3C illustrates a flowchart for detecting a malfunctioning paymentinterface. The flowchart begins with receiving transaction data for acustomer, the transaction data to describe a purchase made by thecustomer along with historical transaction data for the customer(element 3210). In response to receiving the transaction data and thehistorical transaction data for the customer, the malfunction detectionlogic circuitry may perform an inference computation, by a neuralnetwork or mathematical model based on the transaction data. The neuralnetwork or mathematical model may determine a deviation of the set oftransactions or purchases from a range of behavioral patterns learnedduring training to determine a probability that the customer's paymentinstrument has at least one malfunctioning or failing payment interfacebased on a purchase history of the customer (element 3215). Thedeviation may comprise, e.g., an error between typical behavioralpatterns, or transaction patterns, and a behavioral pattern exhibited inthe input transaction data. In further embodiments, the deviation maycomprise, e.g., an error between behavioral patterns, or transactionpatterns, indicative of a malfunctioning payment interface and abehavioral pattern exhibited in the input transaction data. Themalfunction detection logic circuitry may compare the probabilityagainst a probability threshold and, if the probability exceeds theprobability threshold, the malfunction detection logic circuitry maydetermine that the input transaction data indicates that at least onepayment interface of the customer's payment is malfunctioning (element3220).

In response to determining that at least one payment interface of thecustomer's payment is malfunctioning, the malfunction detection logiccircuitry may determine whether to contact the customer (element 3225).For instance, the malfunction detection logic circuitry may cause amessage to transmit to the customer. The message may indicate that a newpayment instrument is being issued to the customer, may requestinformation about the malfunction of payment interfaces of the paymentinstrument, and/or may inquire about issuance of a new paymentinstrument.

FIG. 3D illustrates a flowchart for causing a communication to transmitto a customer about a malfunctioning payment interface or reissuance ofa new payment instrument. In response to determining that a paymentinterface might be malfunctioning, the malfunction detection logiccircuitry may cause a communication to transmit to the customer (element3310). For instance, the malfunction detection logic circuitry mayinform the customer that a new payment instrument is being mailed to thecustomer, inquire about the functionality of the payment interfaces ofthe customer's payment instrument, and/or inquire about the customer'sexperience in receiving a new payment instrument.

FIG. 3E illustrates a flowchart for training a neural network to detectbehavioral patterns, or transaction patterns, in transaction dataassociated with a customer that indicate that the customer's paymentinstrument might have a malfunctioning payment interface. The flowchartbegins with providing transaction data associated with more than onecustomer (element 3410). The malfunction detection logic circuitry mayhave access to a database with transaction data for multiple customersover a period of time.

The malfunction detection logic circuitry may identify for multiplecustomers of the more than one customer, a request to obtain a newpayment instrument (element 3415). The malfunction detection logiccircuitry may the search for sets of transactions that lead to a requestfor reissuance of a payment instrument by the customer (element 3420).For instance, the malfunction detection logic circuitry may identify,e.g., five weeks of transaction data that leads to a request by thecustomers for reissuance of a payment instrument. The five-week periodof transaction data for the customer is likely to include a behavioralpattern, or transaction pattern, that exhibits a behavior, in terms ofconducting transactions, of the customer while a payment interface ofthe customer's payment instrument malfunctions.

After identifying a set of transaction data indicative of a transactionpattern leading to the request to obtain the new payment instrument, themalfunction logic circuitry may train a model based on the set oftransaction data for the multiple customers. The malfunction logiccircuitry may train the model to output, in inference mode, aprobability based on a deviation from transaction patterns that lead tothe requests to obtain new payment instruments (element 3425). Trainingwith the sets of transactions exhibiting a transaction pattern, orbehavioral pattern, indicative of a malfunctioning payment interfaceallows the model to learn a large number of such behavioral patterns,With the large number of behavioral patterns, the model can determine aprobability that input transaction data, while in inference mode,exhibits a behavioral pattern of a malfunctioning payment interface bydetermining a deviation of the behavioral pattern from the learnedbehavioral patterns.

FIG. 4 illustrates an embodiment of a system 4000. The system 4000 is acomputer system with multiple processor cores such as a distributedcomputing system, supercomputer, high-performance computing system,computing cluster, mainframe computer, mini-computer, client-serversystem, personal computer (PC), workstation, server, portable computer,laptop computer, tablet computer, handheld device such as a personaldigital assistant (PDA), or other device for processing, displaying, ortransmitting information. Similar embodiments may comprise, e.g.,entertainment devices such as a portable music player or a portablevideo player, a smart phone or other cellular phone, a telephone, adigital video camera, a digital still camera, an external storagedevice, or the like. Further embodiments implement larger scale serverconfigurations. In other embodiments, the system 4000 may have a singleprocessor with one core or more than one processor. Note that the term“processor” refers to a processor with a single core or a processorpackage with multiple processor cores.

As shown in FIG. 4, system 4000 comprises a motherboard 4005 formounting platform components. The motherboard 4005 is a point-to-pointinterconnect platform that includes a first processor 4010 and a secondprocessor 4030 coupled via a point-to-point interconnect 4056 such as anUltra Path Interconnect (UPI). In other embodiments, the system 4000 maybe of another bus architecture, such as a multi-drop bus. Furthermore,each of processors 4010 and 4030 may be processor packages with multipleprocessor cores including processor core(s) 4020 and 4040, respectively.While the system 4000 is an example of a two-socket (2S) platform, otherembodiments may include more than two sockets or one socket. Forexample, some embodiments may include a four-socket (4S) platform or aneight-socket (8S) platform. Each socket is a mount for a processor andmay have a socket identifier. Note that the term platform refers to themotherboard with certain components mounted such as the processors 4010and the chipset 4060. Some platforms may include additional componentsand some platforms may only include sockets to mount the processorsand/or the chipset.

The first processor 4010 includes an integrated memory controller (IMC)4014 and point-to-point (P-P) interconnects 4018 and 4052. Similarly,the second processor 4030 includes an IMC 4034 and P-P interconnects4038 and 4054. The IMC's 4014 and 4034 couple the processors 4010 and4030, respectively, to respective memories, a memory 4012 and a memory4032. The memories 4012 and 4032 may be portions of the main memory(e.g., a dynamic random-access memory (DRAM)) for the platform such asdouble data rate type 3 (DDR3) or type 4 (DDR4) synchronous DRAM(SDRAM). In the present embodiment, the memories 4012 and 4032 locallyattach to the respective processors 4010 and 4030. In other embodiments,the main memory may couple with the processors via a bus and sharedmemory hub.

The processors 4010 and 4030 comprise caches coupled with each of theprocessor core(s) 4020 and 4040, respectively. In the presentembodiment, the processor core(s) 4020 of the processor 4010 include amalfunction detection logic circuitry 4026 such as the malfunctiondetection logic circuitry 1215 shown in FIG. 1B. The malfunctiondetection logic circuitry 4026 may represent circuitry configured toimplement the functionality of malfunction detection for neural networksupport within the processor core(s) 4020 or may represent a combinationof the circuitry within a processor and a medium to store all or part ofthe functionality of the malfunction detection logic circuitry 4026 inmemory such as cache, the memory 4012, buffers, registers, and/or thelike. In several embodiments, the functionality of the malfunctiondetection logic circuitry 4026 resides in whole or in part as code in amemory such as the malfunction detection logic circuitry 4096 in thedata storage unit 4088 attached to the processor 4010 via a chipset 4060such as the malfunction detection logic circuitry 1225 shown in FIG. 1C.The functionality of the malfunction detection logic circuitry 4026 mayalso reside in whole or in part in memory such as the memory 4012 and/ora cache of the processor. Furthermore, the functionality of themalfunction detection logic circuitry 4026 may also reside in whole orin part as circuitry within the processor 4010 and/or 4030 and mayperform operations, e.g., within registers or buffers such as theregisters 4016 and 4036 within the processors 4010 and 4030,respectively, or within an instruction pipeline of the processor 4010and 4030, respectively.

In other embodiments, more than one of the processor 4010 and 4030 maycomprise the functionality of the malfunction detection logic circuitry4026 such as the processor 4030 and/or the processor within the deeplearning accelerator 4067 coupled with the chipset 4060 via an interface(I/F) 4066. The I/F 4066 may be, for example, a Peripheral ComponentInterconnect-enhanced (PCI-e).

The first processor 4010 couples to a chipset 4060 via P-P interconnects4052 and 4062 and the second processor 4030 couples to a chipset 4060via P-P interconnects 4054 and 4064. Direct Media Interfaces (DMIs) 4057and 4058 may couple the P-P interconnects 4052 and 4062 and the P-Pinterconnects 4054 and 4064, respectively. The DMI may be a high-speedinterconnect that facilitates, e.g., eight Giga Transfers per second(GT/s) such as DMI 3.0. In other embodiments, the processors 4010 and4030 may interconnect via a bus.

The chipset 4060 may comprise a controller hub such as a platformcontroller hub (PCH). The chipset 4060 may include a system clock toperform clocking functions and include interfaces for an I/O bus such asa universal serial bus (USB), peripheral component interconnects (PCIs),serial peripheral interconnects (SPIs), integrated interconnects (I2Cs),and the like, to facilitate connection of peripheral devices on theplatform. In other embodiments, the chipset 4060 may comprise more thanone controller hub such as a chipset with a memory controller hub, agraphics controller hub, and an input/output (I/O) controller hub.

In the present embodiment, the chipset 4060 couples with a trustedplatform module (TPM) 4072 and the unified extensible firmware interface(UEFI), BIOS, Flash component 4074 via an interface (I/F) 4070. The TPM4072 is a dedicated microcontroller designed to secure hardware byintegrating cryptographic keys into devices. The UEFI, BIOS, Flashcomponent 4074 may provide pre-boot code.

Furthermore, chipset 4060 includes an I/F 4066 to couple chipset 4060with a high-performance graphics engine, graphics card 4065. In otherembodiments, the system 4000 may include a flexible display interface(FDI) between the processors 4010 and 4030 and the chipset 4060. The FDIinterconnects a graphics processor core in a processor with the chipset4060.

Various I/O devices 4092 couple to the bus 4081, along with a bus bridge4080 which couples the bus 4081 to a second bus 4091 and an I/F 4068that connects the bus 4081 with the chipset 4060. In one embodiment, thesecond bus 4091 may be a low pin count (LPC) bus. Various devices maycouple to the second bus 4091 including, for example, a keyboard 4082, amouse 4084, communication devices 4086 and a data storage unit 4088 thatmay store code such as the malfunction detection logic circuitry 4096.Furthermore, an audio I/O 4090 may couple to second bus 4091. Many ofthe I/O devices 4092, communication devices 4086, and the data storageunit 4088 may reside on the motherboard 4005 while the keyboard 4082 andthe mouse 4084 may be add-on peripherals. In other embodiments, some orall the I/O devices 4092, communication devices 4086, and the datastorage unit 4088 are add-on peripherals and do not reside on themotherboard 4005.

FIG. 5 illustrates an example of a storage medium 5000 to storeprocessor data structures. Storage medium 5000 may comprise an articleof manufacture. In some examples, storage medium 5000 may include anynon-transitory computer readable medium or machine readable medium, suchas an optical, magnetic or semiconductor storage. Storage medium 5000may store various types of computer executable instructions, such asinstructions to implement logic flows and/or techniques describedherein. Examples of a computer readable or machine readable storagemedium may include any tangible media capable of storing electronicdata, including volatile memory or non-volatile memory, removable ornon-removable memory, erasable or non-erasable memory, writeable orre-writeable memory, and so forth. Examples of computer executableinstructions may include any suitable type of code, such as source code,compiled code, interpreted code, executable code, static code, dynamiccode, object-oriented code, visual code, and the like. The examples arenot limited in this context.

FIG. 6 illustrates an example computing platform 6000. In some examples,as shown in FIG. 6, computing platform 6000 may include a processingcomponent 6010, other platform components or a communications interface6030. According to some examples, computing platform 6000 may beimplemented in a computing device such as a server in a system such as adata center or server farm that supports a manager or controller formanaging configurable computing resources as mentioned above.Furthermore, the communications interface 6030 may comprise a wake-upradio (WUR) and may be capable of waking up a main radio of thecomputing platform 6000.

According to some examples, processing component 6010 may executeprocessing operations or logic for apparatus 6015 described herein suchas the malfunction detection logic circuitry 1015 and 1215 illustratedin FIGS. 1A and 1C. Processing component 6010 may include varioushardware elements, software elements, or a combination of both. Examplesof hardware elements may include devices, logic devices, components,processors, microprocessors, circuits, processor circuits, circuitelements (e.g., transistors, resistors, capacitors, inductors, and soforth), integrated circuits, application specific integrated circuits(ASIC), programmable logic devices (PLD), digital signal processors(DSP), field programmable gate array (FPGA), memory units, logic gates,registers, semiconductor device, chips, microchips, chip sets, and soforth. Examples of software elements, which may reside in the storagemedium 6020, may include software components, programs, applications,computer programs, application programs, device drivers, systemprograms, software development programs, machine programs, operatingsystem software, middleware, firmware, software modules, routines,subroutines, functions, methods, procedures, software interfaces,application program interfaces (API), instruction sets, computing code,computer code, code segments, computer code segments, words, values,symbols, or any combination thereof. Determining whether an example isimplemented using hardware elements and/or software elements may vary inaccordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints, as desired for a given example.

In some examples, other platform components 6025 may include commoncomputing elements, such as one or more processors, multi-coreprocessors, co-processors, memory units, chipsets, controllers,peripherals, interfaces, oscillators, timing devices, video cards, audiocards, multimedia input/output (I/O) components (e.g., digitaldisplays), power supplies, and so forth. Examples of memory units mayinclude without limitation various types of computer readable andmachine readable storage media in the form of one or more higher speedmemory units, such as read-only memory (ROM), random-access memory(RAM), dynamic RAM (DRAM), Double-Data-Rate DRAM (DDRAM), synchronousDRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasableprogrammable ROM (EPROM), electrically erasable programmable ROM(EEPROM), flash memory, polymer memory such as ferroelectric polymermemory, ovonic memory, phase change or ferroelectric memory,silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or opticalcards, an array of devices such as Redundant Array of Independent Disks(RAID) drives, solid state memory devices (e.g., USB memory), solidstate drives (SSD) and any other type of storage media suitable forstoring information.

In some examples, communications interface 6030 may include logic and/orfeatures to support a communication interface. For these examples,communications interface 6030 may include one or more communicationinterfaces that operate according to various communication protocols orstandards to communicate over direct or network communication links.Direct communications may occur via use of communication protocols orstandards described in one or more industry standards (includingprogenies and variants) such as those associated with the PCI Expressspecification. Network communications may occur via use of communicationprotocols or standards such as those described in one or more Ethernetstandards promulgated by the Institute of Electrical and ElectronicsEngineers (IEEE). For example, one such Ethernet standard may includeIEEE 802.3-2012, Carrier sense Multiple access with Collision Detection(CSMA/CD) Access Method and Physical Layer Specifications, Published inDecember 2012 (hereinafter “IEEE 802.3”). Network communication may alsooccur according to one or more OpenFlow specifications such as theOpenFlow Hardware Abstraction API Specification. Network communicationsmay also occur according to Infiniband Architecture Specification,Volume 1, Release 1.3, published in March 2015 (“the InfinibandArchitecture specification”).

Computing platform 6000 may be part of a computing device that may be,for example, a server, a server array or server farm, a web server, anetwork server, an Internet server, a work station, a mini-computer, amain frame computer, a supercomputer, a network appliance, a webappliance, a distributed computing system, multiprocessor systems,processor-based systems, or combination thereof. Accordingly, functionsand/or specific configurations of computing platform 6000 describedherein, may be included or omitted in various embodiments of computingplatform 6000, as suitably desired.

The components and features of computing platform 6000 may beimplemented using any combination of discrete circuitry, ASICs, logicgates and/or single chip architectures. Further, the features ofcomputing platform 6000 may be implemented using microcontrollers,programmable logic arrays and/or microprocessors or any combination ofthe foregoing where suitably appropriate. It is noted that hardware,firmware and/or software elements may be collectively or individuallyreferred to herein as “logic”.

It should be appreciated that the exemplary computing platform 6000shown in the block diagram of FIG. 6 may represent one functionallydescriptive example of many potential implementations. Accordingly,division, omission or inclusion of block functions depicted in theaccompanying figures does not infer that the hardware components,circuits, software and/or elements for implementing these functionswould necessarily be divided, omitted, or included in embodiments.

One or more aspects of at least one example may be implemented byrepresentative instructions stored on at least one machine-readablemedium which represents various logic within the processor, which whenread by a machine, computing device or system causes the machine,computing device or system to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores”, may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that actually make the logic or processor.

Various examples may be implemented using hardware elements, softwareelements, or a combination of both. In some examples, hardware elementsmay include devices, components, processors, microprocessors, circuits,circuit elements (e.g., transistors, resistors, capacitors, inductors,and so forth), integrated circuits, application specific integratedcircuits (ASIC), programmable logic devices (PLD), digital signalprocessors (DSP), field programmable gate array (FPGA), memory units,logic gates, registers, semiconductor device, chips, microchips, chipsets, and so forth. In some examples, software elements may includesoftware components, programs, applications, computer programs,application programs, system programs, machine programs, operatingsystem software, middleware, firmware, software modules, routines,subroutines, functions, methods, procedures, software interfaces,application program interfaces (API), instruction sets, computing code,computer code, code segments, computer code segments, words, values,symbols, or any combination thereof. Determining whether an example isimplemented using hardware elements and/or software elements may vary inaccordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints, as desired for a givenimplementation.

Some examples may include an article of manufacture or at least onecomputer-readable medium. A computer-readable medium may include anon-transitory storage medium to store logic. In some examples, thenon-transitory storage medium may include one or more types ofcomputer-readable storage media capable of storing electronic data,including volatile memory or non-volatile memory, removable ornon-removable memory, erasable or non-erasable memory, writeable orre-writeable memory, and so forth. In some examples, the logic mayinclude various software elements, such as software components,programs, applications, computer programs, application programs, systemprograms, machine programs, operating system software, middleware,firmware, software modules, routines, subroutines, functions, methods,procedures, software interfaces, API, instruction sets, computing code,computer code, code segments, computer code segments, words, values,symbols, or any combination thereof.

According to some examples, a computer-readable medium may include anon-transitory storage medium to store or maintain instructions thatwhen executed by a machine, computing device or system, cause themachine, computing device or system to perform methods and/or operationsin accordance with the described examples. The instructions may includeany suitable type of code, such as source code, compiled code,interpreted code, executable code, static code, dynamic code, and thelike. The instructions may be implemented according to a predefinedcomputer language, manner or syntax, for instructing a machine,computing device or system to perform a certain function. Theinstructions may be implemented using any suitable high-level,low-level, object-oriented, visual, compiled and/or interpretedprogramming language.

Some examples may be described using the expression “in one example” or“an example” along with their derivatives. These terms mean that aparticular feature, structure, or characteristic described in connectionwith the example is included in at least one example. The appearances ofthe phrase “in one example” in various places in the specification arenot necessarily all referring to the same example.

Some examples may be described using the expression “coupled” and“connected” along with their derivatives. These terms are notnecessarily intended as synonyms for each other. For example,descriptions using the terms “connected” and/or “coupled” may indicatethat two or more elements are in direct physical or electrical contactwith each other. The term “coupled,” however, may also mean that two ormore elements are not in direct contact with each other, but yet stillco-operate or interact with each other.

In addition, in the foregoing Detailed Description, it can be seen thatvarious features are grouped together in a single example for thepurpose of streamlining the disclosure. This method of disclosure is notto be interpreted as reflecting an intention that the claimed examplesrequire more features than are expressly recited in each claim. Rather,as the following claims reflect, inventive subject matter lies in lessthan all features of a single disclosed example. Thus, the followingclaims are hereby incorporated into the Detailed Description, with eachclaim standing on its own as a separate example. In the appended claims,the terms “including” and “in which” are used as the plain-Englishequivalents of the respective terms “comprising” and “wherein,”respectively. Moreover, the terms “first,” “second,” “third,” and soforth, are used merely as labels, and are not intended to imposenumerical requirements on their objects.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code to reduce the number of times code must be retrievedfrom bulk storage during execution. The term “code” covers a broad rangeof software components and constructs, including applications, drivers,processes, routines, methods, modules, firmware, microcode, andsubprograms. Thus, the term “code” may be used to refer to anycollection of instructions which, when executed by a processing system,perform a desired operation or operations.

Logic circuitry, devices, and interfaces herein described may performfunctions implemented in hardware and also implemented with codeexecuted on one or more processors. Logic circuitry refers to thehardware or the hardware and code that implements one or more logicalfunctions. Circuitry is hardware and may refer to one or more circuits.Each circuit may perform a particular function. A circuit of thecircuitry may comprise discrete electrical components interconnectedwith one or more conductors, an integrated circuit, a chip package, achip set, memory, or the like. Integrated circuits include circuitscreated on a substrate such as a silicon wafer and may comprisecomponents. And integrated circuits, processor packages, chip packages,and chipsets may comprise one or more processors.

Processors may receive signals such as instructions and/or data at theinput(s) and process the signals to generate the at least one output.While executing code, the code changes the physical states andcharacteristics of transistors that make up a processor pipeline. Thephysical states of the transistors translate into logical bits of onesand zeros stored in registers within the processor. The processor cantransfer the physical states of the transistors into registers andtransfer the physical states of the transistors to another storagemedium.

A processor may comprise circuits to perform one or more sub-functionsimplemented to perform the overall function of the processor. Oneexample of a processor is a state machine or an application-specificintegrated circuit (ASIC) that includes at least one input and at leastone output. A state machine may manipulate the at least one input togenerate the at least one output by performing a predetermined series ofserial and/or parallel manipulations or transformations on the at leastone input.

The logic as described above may be part of the design for an integratedcircuit chip. The chip design is created in a graphical computerprogramming language and stored in a computer storage medium or datastorage medium (such as a disk, tape, physical hard drive, or virtualhard drive such as in a storage access network). If the designer doesnot fabricate chips or the photolithographic masks used to fabricatechips, the designer transmits the resulting design by physical means(e.g., by providing a copy of the storage medium storing the design) orelectronically (e.g., through the Internet) to such entities, directlyor indirectly. The stored design is then converted into the appropriateformat (e.g., GDSII) for the fabrication.

The resulting integrated circuit chips can be distributed by thefabricator in raw wafer form (that is, as a single wafer that hasmultiple unpackaged chips), as a bare die, or in a packaged form. In thelatter case, the chip is mounted in a single chip package (such as aplastic carrier, with leads that are affixed to a motherboard or otherhigher-level carrier) or in a multichip package (such as a ceramiccarrier that has either or both surface interconnections or buriedinterconnections). In any case, the chip is then integrated with otherchips, discrete circuit elements, and/or other signal processing devicesas part of either (a) an intermediate product, such as a processorboard, a server platform, or a motherboard, or (b) an end product.

What is claimed is:
 1. A system comprising: memory to storeinstructions; and circuitry coupled with the memory, the circuitryconfigured to execute the instructions, that when executing theinstructions, cause the circuitry to: obtain transaction datacorresponding to a plurality of transaction performed via a paymentinstrument associated with a customer; apply a neural network to thetransaction data, the neural network trained based on purchase historiesof multiple customers to train the neural network to recognize patternsof transactions that represent behavioral patterns of the customers;determine an interface of a plurality of interfaces of the paymentinstruction is malfunctioning based on applying the neural network; andcause communication to a device associated with the customer.
 2. Thesystem of claim 1, the circuitry to: obtain purchase histories of themultiple customers from a storage structure; identify for the multiplecustomers one customers having at least one request to obtain a newpayment instrument; determine a set of transaction data indicative of atleast one transaction pattern leading to the requests to obtain the newpayment instruments; and train the neural network based on the set oftransaction data for the multiple customers.
 3. The system of claim 2,wherein the neural network, based on the training with the set oftransaction data, to output a probability based on a deviation from atleast one transaction pattern that lead to the requests to obtain newpayment instruments.
 4. The system of claim 1, the circuitry todetermine a probability of a malfunction of the interface exceeds athreshold based on the application of the neural network.
 5. The systemof claim 1, wherein the one or more payment interfaces comprise at leastone contactless interface and at least one contacted interface, the atleast one contactless interface comprising a near field communication(NFC) interface, a radio frequency identifier (RFID) tag, or combinationof both, and the at least one contact interface comprising a magneticstripe, a chip, or a combination of both.
 6. The system of claim 1,wherein the neural network is pretrained based on transaction data fromthe multiple customers, the transaction data including changes intransaction patterns, the changes in transaction patterns includinginformation about transaction modes, wherein each transaction mode isassociated with one of the one or more payment interfaces.
 7. The systemof claim 1, wherein neural network is pretrained based on transactiondata from the multiple customers, the transaction data including changesin transaction patterns prior to a decrease in a frequency of use of thepayment instrument or a decrease in use of one interface of the one ormore payment interfaces associated with the payment instrument.
 8. Thesystem of claim 1, the circuitry to pretrain the neural network based onthe purchase histories, of the multiple customers, that includetransactions conducted via more than one transaction mode.
 9. Acomputer-implemented method, comprising: receiving transaction datacorresponding to a plurality of transaction performed via a paymentinstrument associated with a customer; processing the transaction datathrough a neural network, the neural network trained based on purchasehistories of multiple customers to train the neural network to recognizepatterns of transactions that represent behavioral patterns of thecustomers; detecting an interface of a plurality of interfaces of thepayment instruction is malfunctioning based on applying the neuralnetwork; and sending, based on the detection, an indication to a deviceassociated with the customer, the indication o indicate that theinterface is malfunctioning.
 10. The computer-implemented method ofclaim 9, comprising: obtaining purchase histories of the multiplecustomers from a storage structure; identifying for the multiplecustomers one customers having at least one request to obtain a newpayment instrument; determining a set of transaction data indicative ofat least one transaction pattern leading to the requests to obtain thenew payment instruments; and training the neural network based on theset of transaction data for the multiple customers.
 11. Thecomputer-implemented method of claim 10, wherein the neural network,based on the training with the set of transaction data, to output aprobability based on a deviation from at least one transaction patternthat lead to the requests to obtain new payment instruments.
 12. Thecomputer-implemented method of claim 9, comprising determining aprobability of a malfunction of the interface exceeds a threshold basedon the application of the neural network.
 13. The computer-implementedmethod of claim 9, wherein the one or more payment interfaces compriseat least one contactless interface and at least one contacted interface,the at least one contactless interface comprising a near fieldcommunication (NFC) interface, a radio frequency identifier (RFID) tag,or combination of both, and the at least one contact interfacecomprising a magnetic stripe, a chip, or a combination of both.
 14. Thecomputer-implemented method of claim 9, wherein the neural network ispretrained based on transaction data from the multiple customers, thetransaction data including changes in transaction patterns, the changesin transaction patterns including information about transaction modes,wherein each transaction mode is associated with one of the one or morepayment interfaces.
 15. The computer-implemented method of claim 9,wherein neural network is pretrained based on transaction data from themultiple customers, the transaction data including changes intransaction patterns prior to a decrease in a frequency of use of thepayment instrument or a decrease in use of one interface of the one ormore payment interfaces associated with the payment instrument.
 16. Thecomputer-implemented method of claim 9, comprising pretraining theneural network based on the purchase histories, of the multiplecustomers, that include transactions conducted via more than onetransaction mode.
 17. A non-transitory machine-readable storage mediumcomprising instructions that, when executed by a processor, cause theprocessor to: obtain transaction data corresponding to a plurality oftransaction performed via a payment instrument associated with acustomer; apply a neural network to the transaction data, the neuralnetwork trained based on purchase histories of multiple customers totrain the neural network to recognize patterns of transactions thatrepresent behavioral patterns of the customers; determine an interfaceof a plurality of interfaces of the payment instruction ismalfunctioning based on applying the neural network; and causecommunication to a device associated with the customer.
 18. Themachine-readable storage medium of claim 17, the processor to: obtainpurchase histories of the multiple customers from a storage structure;identify for the multiple customers one customers having at least onerequest to obtain a new payment instrument; determine a set oftransaction data indicative of at least one transaction pattern leadingto the requests to obtain the new payment instruments; and train theneural network based on the set of transaction data for the multiplecustomers.
 19. The machine-readable storage medium of claim 18, whereinthe neural network, based on the training with the set of transactiondata, to output a probability based on a deviation from at least onetransaction pattern that lead to the requests to obtain new paymentinstruments.
 20. The machine-readable storage medium of claim 17, theprocessor to determine a probability of a malfunction of the interfaceexceeds a threshold based on the application of the neural network.